SOM neural network (self-organizing feature map neural network) is an unsupervised neural network. Network topology is an input layer and an output layer. Input layer nodes is the input dimension of the sample, each node represents a component input samples. Output layer nodes are arranged in two-dimensional array structure. X in the input layer and output layer each node of each neuron node Y by a weight (the weight vector as W) is connected, so that each output layer corresponds to a connection node of the right vector. Self-organizing feature maps of the basic principle is, when each category of inputs into the model, its output layer one node get the maximum boost and win, Huoshengjiedian around Yixiejiedian Yin Zuo Yong Ye Shoudaojiaotai lateral stimulation. Then a learning network operation, the winner node and surrounding nodes in the right direction vector to the input mode to make consequential amendments. When the input mode type changes, the two-dimensional plane of the wi

SOM
...\MainFrm.cpp
...\MainFrm.h
...\PainterFacade.cpp
...\PainterFacade.h
...\RandMaker.cpp
...\RandMaker.h
...\ReadMe.txt
...\res
...\...\SOM.ico
...\...\SOM.rc2
...\...\SOMDoc.ico
...\...\Toolbar.bmp
...\Resource.h
...\SOM.APS
...\SOM.clw
...\SOM.cpp
...\SOM.dsp
...\SOM.dsw
...\SOM.exe
...\SOM.h
...\SOM.rc
...\SOMDoc.cpp
...\SOMDoc.h
...\SOMView.cpp
...\SOMView.h
...\StdAfx.cpp
...\StdAfx.h

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